Improving Gastroesophageal Reflux Diseases Classification Diagnosis from Endoscopic Images Using StyleGAN2-ADA

Nguyen, Phuong-Thao; Tran, Thanh-Hai; Hang, Dao Viet; Vu, Hai

doi:10.1007/978-3-030-97610-1_30

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…closely with the findings of Hirasawa et al In a separate study by Nguyen et al[38], a method was proposed for the automatic classification of GERD from endoscopic images. They utilized a deep neural network (ResNet-50) and their approach resulted in a notable improvement in classification accuracy, increasing from 83.2% to 91.7% specifically on RGB channels.Liao and …”

supporting

confidence: 69%

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Maity,

Raja Sankari,

et al. 2024

Biomed. Phys. Eng. Express

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Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.

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supporting

confidence: 69%

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Maity,

Raja Sankari,

et al. 2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

show abstract

“…ResNet-50 was used for feature extraction and classification. Two data augmentation techniques, Affine Transformation and Generative Adversarial Network (i.e., StyleGAN2-ADA) on different color models (RGB, HSV), were used to improve the classification of GERD due to which there was a significant improvement from 83.2% to 91.7% in accuracy when classifying GERD using StyleGAN-ADA on RGB channels compared to the original data on the dataset [27].…”

Section: Ai Models Used To Classify Gerdmentioning

confidence: 99%

Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease

Kommuru,

Adekunle,

Niño

et al. 2024

Cureus

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Automatic classification of upper gastrointestinal tract diseases from endoscopic images

Nguyen¹,

Le²,

Dao

et al. 2022

2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)

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Improving Gastroesophageal Reflux Diseases Classification Diagnosis from Endoscopic Images Using StyleGAN2-ADA

Cited by 4 publications

References 17 publications

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease

Automatic classification of upper gastrointestinal tract diseases from endoscopic images

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